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Automatic and Accurate Extraction of Road Intersections from Raster Maps

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Abstract

Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides key information about the road network, which includes the location of the road junctions, the number of roads that meet at the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction, our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster maps from a variety of sources.

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Notes

  1. http://terraserver-usa.com/

  2. http://tiger.census.gov/cgi-bin/mapsurfer

  3. http://map.google.com

  4. http://map.yahoo.com

  5. http://www.mapquest.com

  6. RW is used in this paper as a variable representing the road width in pixels.

  7. http://sourceforge.net/projects/opencvlibrary, GoodFeaturesToTrack function.

  8. ESRI Map, MapQuest Map, TIGER/Line Map, Yahoo Map, A9 Map, MSN Map, Google Map, Map24 Map, ViaMichelin Map, Multimap Map, USGS Topographic Map, Thomas Brothers Map.

  9. Some of the sources do not provide resolution information.

  10. The terms precision and recall are common evaluation terminologies in information retrieval and correctness and completeness are often used alternatively in geomatics and remote sensing [12].

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Acknowledgements

This research is based upon work supported in part by the United States Air Force under contract number FA9550-08-C-0010, in part by the National Science Foundation under Award No. IIS-0324955, in part by the Air Force Office of Scientific Research under grant number FA9550-07-1-0416, in part by a gift from Microsoft, and in part by the Department of Homeland Security under ONR grant number N00014-07-1-0149. The U.S. Government is authorized to reproduce and distribute reports for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of any of the above organizations or any person connected with them. We would like to thank Dr. Chew Lim Tan for his generous sharing of their code in [6].

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Correspondence to Yao-Yi Chiang.

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This work is based on an earlier paper: Automatic Extraction of Road Intersections from Raster Maps, in Proceedings of the 13th annual ACM International Symposium on Geographic Information Systems, Pages: 267–276, ©ACM, 2005. http://doi.acm.org/10.1145/1097064.1097102.

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Chiang, YY., Knoblock, C.A., Shahabi, C. et al. Automatic and Accurate Extraction of Road Intersections from Raster Maps. Geoinformatica 13, 121–157 (2009). https://doi.org/10.1007/s10707-008-0046-3

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  • DOI: https://doi.org/10.1007/s10707-008-0046-3

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